Advancements in incorporating metal ions onto the surface of biomedical titanium and its alloys via micro-arc oxidation: a research review

The incorporation of biologically active metallic elements into nano/micron-scale coatings through micro-arc oxidation (MAO) shows significant potential in enhancing the biological characteristics and functionality of titanium-based materials. By introducing diverse metal ions onto titanium implant surfaces, not only can their antibacterial, anti-inflammatory and corrosion resistance properties be heightened, but it also promotes vascular growth and facilitates the formation of new bone tissue. This review provides a thorough examination of recent advancements in this field, covering the characteristics of commonly used metal ions and their associated preparation parameters. It also highlights the diverse applications of specific metal ions in enhancing osteogenesis, angiogenesis, antibacterial efficacy, anti-inflammatory and corrosion resistance properties of titanium implants. Furthermore, the review discusses challenges faced and future prospects in this promising area of research. In conclusion, the synergistic approach of micro-arc oxidation and metal ion doping demonstrates substantial promise in advancing the effectiveness of biomedical titanium and its alloys, promising improved outcomes in medical implant applications

Object Detection for Construction Waste Based on an Improved YOLOv5 Model

An object detection method based on an improved YOLOv5 model was proposed to enhance the accuracy of sorting construction waste. A construction waste image sample set was established by collecting construction waste images on site. These construction waste images were preprocessed using the random brightness method. A YOLOv5 object detection model was improved in terms of the convolutional block attention module (CBAM), simplified SPPF (SimSPPF) and multi-scale detection. Then, the improved YOLOv5 model was trained, validated and tested using the established construction waste image dataset and compared with other conventional models such as Faster-RCNN, YOLOv3, YOLOv4, and YOLOv7. The results show that: based on the improved YOLOv5 model, the mean average precision (mAP) on the test dataset can reach 0.9480. The overall performance of this model is better than that of other conventional models in object detection, which verifies the accuracy and availability of the proposed method

Analyzing the thermal and hygral behavior of wool and its impact on fabric dimensional stability for wool processing and garment manufacturing

Wool is one of the most moisture sensitive natural fibers. This paper investigated changes of wool fiber diameter, fabric dimensions and fabric dimensional properties, as a function of moisture regain, temperature and pH. Experiments were conducted on fabrics with different weave structures as well as on fabrics with and without a permanent set. Results showed that the fabrics tended to contract when they were subjected to increased temperature at saturated regain. The degree of contraction appeared to depend on the weave structure of the fabrics and permanent setting treatments. Dimensions of the wool fabrics were also found to be dependent on the pH. Greater fabric dimensions were observed at pH&thinsp;7.2 than at pH&thinsp;2.1. The contraction effect was almost reversible when unset fabric samples were measured in pH&thinsp;2.1. The reasons for the changes of dimensional property were analyzed in terms of changes in wool fiber swelling, yarn crimp and polymer relaxation phenomena with changes in regain, temperature and pH. Industrial implications from outcomes of this research to practical wool processing are discussed in the paper.</jats:p

Effect of micropore/microsphere topography and a silicon-incorporating modified titanium plate surface on the adhesion and osteogenic differentiation of BMSCs

AbstractGood biological properties for titanium implants will shorten the treatment cycle and improve patient comfort, which are also the main goals of dentistry and orthopaedics. At present, the biological properties of titanium implants are mainly enhanced in two aspects: their surface chemistry and surface morphology. In this study, a surface modification strategy combining bioactive trace elements with surface micromorphology modification was used to enhance the biological properties of pure titanium. A new coating incorporating silicon micropore/microsphere topography was prepared on a titanium plate by micro-arc oxidation (MAO) technology. The properties of the coating and its effects on the adhesion and osteogenic differentiation of rat bone marrow mesenchymal stem cells (BMSCs) were further analyzed. The experimental results show that a coating doped with amorphous silicon with micropore/microsphere topography was incorporated onto the titanium surface and the surface roughness in the treated groups was obviously higher than that in the Ti group. In vitro, the presence of a silicon-incorporating coating with a micropore/microsphere topography on the titanium surface significantly enhanced the initial adhesion, proliferation and osteogenic differentiation of BMSCs. These results indicate that the silicon-incorporating coating with micropore/microsphere topography has potential applications in dentistry and orthopaedics

Living/Controlled Polymerization of Renewable Lignin-Based Monomers by Lewis Pairs

It is a challenging task to replace the traditional petroleum-based monomers with the biorenewable monomers for polymer synthesis, as it can alleviate the energy and environmental crises. Lewis pairs (LP) composed of organophosphorus superbases and organoaluminum Lewis acid are employed to rapidly and quantitatively transform a series of biorenewable monomers derived from the lignin degradation products into polymers with predicted molecular weight (Mn up to 519 kg mol–1) and small Đ value (as low as 1.10). The livingness of the polymerization of lignin-based monomer by such LP system can be also verified by the following evidence, including a linear increase of polymer Mn vs monomer-to-initiator ratio and monomer conversion and high end-group fidelity as evidenced by successful chain extensions and synthesis of well-defined block copolymers. More impressively, the lignin-based copolymers with methyl ferulate exhibited fluorescence response under the irradiation of UV light at 365 nm, suggesting application potential of the lignin-based polymers in biological imaging, information storage, and anti-counterfeiting materials

Nitrogen deposition drives the intricate changes of fine root traits

Increase in nitrogen (N) deposition will cause changes of root morphological and functional traits, thus deeply affecting ecosystem carbon (C) and N cycles. However, the influence of N deposition to root traits under different climatic conditions, and with different N deposition rate and durations were still unclear. Here, a meta-analysis was conducted to evaluate the effects of simulated increase in N deposition on 11 root traits under different conditions. In general, N addition significantly increased root/shoot ratio, fine root diameter, total root biomass, fine root production, fine root turnover rate, root respiration, fine root N concentration, while decreased fine root C/N at the global scale. Under N addition, the increased extents of fine root biomass and total root biomass were significantly greater in grassland ecosystems, while the increased extent of fine root turnover was greater in forests. N addition significantly increased fine root production in snow climate zone where forests with ectomycorrhizae. A pattern may be inferred that with increases in mean annual temperature (MAT) and mean annual precipitation (MAP), N addition decreased fine root N concentration and fine root C/N, while increased fine root turnover at the high-MAT and high-MAP areas. In addition, it may be ascertained that fine roots became shorter in the low-rate and short-term N addition experiments, while roots became longer in the high-rate and long-term N addition experiments. Our study indicates that increase in N deposition will cause intricate changes of root traits due to the diversity of climatic conditions and the uncertainty of increase rate and duration of N deposition in future

Weakly Correlated Multimodal Sentiment Analysis: New Dataset and Topic-oriented Model

Existing multimodal sentiment analysis models focus more on fusing highly correlated image-text pairs, and thus achieves unsatisfactory performance on multimodal social media data which usually manifests weak correlations between different modalities. To address this issue, we first build a large multimodal social media sentiment analysis dataset RU-Senti which contains more than 100,000 image-text pairs with sentiment labels. Then, we proposed a topic-oriented model (TOM) which assumes that text is usually related to a certain portion of the image contents and the image-text pairs of the same topic often have similar sentiment tendencies. TOM learns the topic information from textual content and designs a topic-oriented feature alignment module to extract textual semantics correlated information from images, thus achieving the alignment between two modalities. Then, TOM utilizes a transformer encoder initialized with the parameters from a pre-trained vision-language model to fuse the multimodal features for sentiment prediction. According to the experiments over the public MVSA-Multiple dataset and our RU-Senti dataset, RU-Senti is of high suitability for studying weakly correlated multimodal sentiment analysis, and the proposed TOM model also largely outperforms the SOTA mulitimodal sentiment analysis methods and pre-trained vision-language models. The RU-Senti dataset and the code of TOM are available at https://github.com/PhenoixYANG/TOM. </p